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Enhanced recommender system using predictive network approach

Author

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  • Zare, Hadi
  • Nikooie Pour, Mina Abd
  • Moradi, Parham

Abstract

Recommender systems have a unique role in on-line trading companies due to building relationships among users and items to reduce big information load. There exist several successful algorithms in the recommender systems like collaborative filtering (CF), although most of them suffer from the sparsity problem. Here, we propose a novel integrated recommendation approach based on the tools of network science to mitigate the sparsity problem. The link prediction approach is used to extract hidden structures among users, and diffusion of information is applied to enhance the rating matrix in our proposed framework. Not only, the sparsity problem is alleviated through a more efficient way, but the proposed approach also can be applied in a hybrid way with the well-known algorithms. The proposed approach is examined on several datasets via standard evaluation criteria. The experimental results show that the proposed approach outperforms the earlier methods.

Suggested Citation

  • Zare, Hadi & Nikooie Pour, Mina Abd & Moradi, Parham, 2019. "Enhanced recommender system using predictive network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 322-337.
    • Handle: RePEc:eee:phsmap:v:520:y:2019:i:c:p:322-337
    DOI: 10.1016/j.physa.2019.01.053
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.
    3. Zhang, Peng & Wang, Duo & Xiao, Jinghua, 2017. "Improving the recommender algorithms with the detected communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 147-153.
    4. Yu, Fei & Zeng, An & Gillard, Sébastien & Medo, Matúš, 2016. "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 192-208.
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    Cited by:

    1. Su, Zhan & Zheng, Xiliang & Ai, Jun & Shen, Yuming & Zhang, Xuanxiong, 2020. "Link prediction in recommender systems based on vector similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).

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